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Related Concept Videos

Observational Learning01:12

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...
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Perception01:28

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Automatic Processing and Automatic Social Behavior

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Related Experiment Video

Updated: May 20, 2026

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

Learning emergent behaviours for a hierarchical Bayesian framework for active robotic perception.

João Filipe Ferreira1, Christiana Tsiourti, Jorge Dias

  • 1ISR, University of Coimbra, Coimbra, Portugal. jfilipe@isr.uc.pt

Cognitive Processing
|July 19, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a novel behavior learning process for a hierarchical Bayesian framework, enabling adaptive and scalable multimodal active perception. The system learns human-like active perception behaviors for various tasks, demonstrating goal-dependent control.

Related Experiment Videos

Last Updated: May 20, 2026

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

Area of Science:

  • Cognitive Science
  • Robotics
  • Artificial Intelligence

Background:

  • Multimodal active perception is crucial for intelligent systems.
  • Existing frameworks often lack adaptability and scalability.
  • Hierarchical Bayesian approaches offer a robust foundation for complex behavior modeling.

Purpose of the Study:

  • To develop an emergent, scalable, and adaptive behavior learning process for a hierarchical Bayesian framework.
  • To enable the framework to reproduce goal-dependent, human-like active perception behaviors.
  • To integrate multimodal sensor data within a unified spatial configuration for perception and action.

Main Methods:

  • A hierarchical Bayesian framework was employed for multimodal active perception.
  • A novel behavior learning process was developed, focusing on learning "attentional sets" (model parameters).
  • 3D audiovisual virtual scenarios and head-mounted displays were used to collect human fixation data for training.

Main Results:

  • The learning process successfully reproduced goal-dependent human-like active perception behaviors.
  • The framework demonstrated the ability to adapt "attentional sets" dynamically for different tasks.
  • High-level behaviors emerged from the interaction of simpler building blocks within the framework.

Conclusions:

  • The proposed hierarchical Bayesian framework with its learning process provides an adaptive and scalable solution for multimodal active perception.
  • The system can learn and dynamically adjust attentional sets, enabling flexible, goal-dependent behaviors.
  • This research contributes to developing more sophisticated and human-like artificial perception systems.